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Self-selection, socialization, and risk perception: an empirical study by Ursula Weisenfeld and Ingrid Ott
No. 1555| September 2009
Kiel Institute for the World Economy, Düsternbrooker Weg 120, 24105 Kiel, Germany
Kiel Working Paper No. 1555| September 2009
Self-selection, socialization, and risk perception: an empirical study
Ursula Weisenfeld and Ingrid Ott
Abstract
We analyze students’ knowledge and risk perception of four technologies. The aim is to find out whether there is a relationship between area of study (self-selection) and progress of study (socialization) on the one hand and risk perception of technologies regarding health, environment and society on the other. The four technology fields under study are renewable energies, genetic engineering, nanotechnology and information and communication technologies (ICT). Key results are: Irrespective of study area, study progress and gender, genetic engineering has the highest perceived risk and renewable energies has the lowest. This holds for all the risks studied (environmental, health, societal risks). For most risk perception variables, advanced students perceive lower risks than beginners, and students in a technical study area perceive lower risks than students in a non-technical area. Factor analyses show that common dimensions of risk are the technological areas and not the type of risk. Regression analyses show that the variables influencing perceived risks vary between the technological fields.
Keywords: technologies, risk perception, self-selection, socialization
JEL-code: O33
Acknowledgements: We would like to thank participants of the 2009 S.NET conference as well as participants of the research seminars at the IfW and the TU Hamburg for valuable comments. Ursula Weisenfeld Leuphana University Lueneburg Scharnhorststr. 1 21335 Lueneburg, Germany Telephone: +49 4131-677- 2120 E-Mail: [email protected]
Ingrid Ott Kiel Institute for the World Economy 24100 Kiel, Germany E-Mail: [email protected]
and Hamburg Institute of International Economics (HWWI)
____________________________________ The responsibility for the contents of the working papers rests with the author, not the Institute. Since working papers are of a preliminary nature, it may be useful to contact the author of a particular working paper about results or caveats before referring to, or quoting, a paper. Any comments on working papers should be sent directly to the author. Coverphoto: uni_com on photocase.com
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Self-selection, socialization, and risk perception of
technologies: An empirical study
Ursula Weisenfelda
Ingrid Ottb
aLeuphana University of Lueneburg, [email protected] bInstitute for the World Economy (IfW), Kiel,
and Hamburg Institute of International Economics (HWWI), Hamburg
September 14, 2009
Abstract
We analyze students’ knowledge and risk perception of four technologies. The aim is to find out whether there is a relationship between area of study (self-selection) and progress of study (socialization) on the one hand and risk perception of technologies regarding health, environment and society on the other. The four technology fields under study are renewable energies, genetic engineering, nanotechnology and information and communication technologies (ICT). Key results are: Irrespective of study area, study progress and gender, genetic engineering has the highest perceived risk and renewable energies has the lowest. This holds for all the risks studied (environmental, health, societal risks). For most risk perception variables, advanced students perceive lower risks than beginners, and students in a technical study area perceive lower risks than students in a non-technical area. Factor analyses show that common dimensions of risk are the technological areas and not the type of risk. Regression analyses show that the variables influencing perceived risks vary between the technological fields.
Keywords: technologies, risk perception, self-selection, socialization
JEL-code: O33
Acknowledgements: We would like to thank participants of the 2009 S.NET conference in Seattle as well as participants of the research seminar the TU Hamburg Harburg for valuable comments.
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1. Introduction
The pattern of technological development has been the subject of studies with different
emphases, ranging from niche formation (Kemp et al. 1998) to multi-level perspective
on transition (Geels and Schot 2007). This article focuses on antecedents of potential
actors’ and stakeholders’ behaviour by analyzing the effects of self-selection and
socialization on the perceived risks of new technologies. New technologies and
respective innovations come with chances and risks that are perceived differently by
various groups and individuals and are shaped by various actors. Risk perception affects
decision making of people involved in activities related to the research, development,
introduction, regulation and use of technologies. Therefore, research on risk perception
has become increasingly important for the management of technology.
Perceptions of chances and risks, held by various stakeholders (researchers, a
company’s managers of different functions, customers, ‘the public’) may differ to a
great extent and are subject to many influences. Perceptions usually are based on one’s
frame of reference and on (incomplete) information. Over time, perceptions and
evaluations can change, e.g. on account of additional information (Chatterjee and
Eliashberg 1990, Roberts and Urban 1988). Indeed, the expectation that knowledge
(relevant information) plays a key role in risk perception has led to numerous studies
with mixed results (Schütz et al. 2000) and to initiatives such as the Public
Understanding of Science campaign launched by the British government. However, the
relationship between knowledge about science and technology on the one hand and
respective risk perceptions on the other hand is complex. While Allum et al. (2008) in a
meta-analysis across cultures find a small but positive relationship between knowledge
and attitude towards technology, they also note that cross-country variation is only 10%
which in turn can be accounted for by the percentage of people in tertiary education. In
our study we take a closer look at that group: We focus on student groups in Germany.
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They all have acquired a certain educational degree (usually ‘Abitur’ or ‘Fachabitur’, a
prerequisite to enrol at university or polytechnic) that makes them a more homogeneous
group regarding knowledge compared to the general public, thereby providing the
opportunity to look for other influencing factors on risk perception. Furthermore, in the
future, many of today’s students will be involved in activities and decisions concerning
new technologies. Especially top management positions, engineering and high positions
in regulatory institutions are associated with university degrees.
We propose that a typical student choosing a topic in the area of science and technology
and a typical student choosing a non-technical subject will differ with regard to their
perceptions and attitudes of technologies (self-selection) and that within an area of
study, risk perception will be different between beginners and advanced students
(socialization). Thus, we differentiate (1) between students in a non-technical area,
namely cultural sciences, business administration, and social sciences, and students in a
technical area, i.e., engineering and (2) between first term students and students in their
third term and above.
Section 2 summarizes key findings in the area of risk perception and section 3 describes
self-selection and socialization as potentially important factors in the explanation of
attitudes and behaviours. Section 4 gives a short description of the four technologies
investigated here. Section 5 presents the empirical study and section 6 draws
conclusions.
2 Risk Perception There is no perfect knowledge about the development and use of technologies. Owing
to high complexity, there is a lack of information at any point of time. Different people
have different bits of knowledge, leading to asymmetry of information. If one were to
collect all the information, things would be already in the process of changing which
involves uncertainty. Thus, information asymmetry (varying information about the
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status quo) and uncertainty (lack of information about the future) lead to risk being an
ubiquitous phenomenon.
‘Experts’ often assess risk as the expected value of the negative outcomes (the harms) of
a decision. This process involves judgement (Fischhoff et al. 1978), and thus the results
will vary between individuals and across contexts. Information is incomplete and
developments are uncertain, hence predictions are based on assumptions. Experts might
differ on account of different (scientific) judgement, different reference systems, or their
dissent might involve politics. Even if there was a consensus amongst experts: the
technical concept of risk is of limited use for policy making (Kasperson et al. 1988),
rather, the perception of risk is influenced by other factors next to probabilities and
magnitudes of risks.
According to the psychometric paradigm, “risk is subjectively defined by individuals
who may be influenced by a wide array of psychological, social, institutional and
cultural factors” (Slovic 2000, xxiii). Analyses of hazards with different characteristics
(inter-hazard variation) point to a limited number of risk dimensions such as
voluntariness (of taking a risk), controllability and familiarity with risk (Slovic 1987,
Renn 1990). Analyses of individuals (inter-individual variation) yield mixed results
with regard to the relationship between factual knowledge and risk perception. Schütz
et al. (2000) assume that next to methodological differences between studies, the type of
risk and situational factors may play a role. The familiarity hypothesis holds that
support for a technology will increase with growing awareness of the technology. For
example, support for nanotechnology was positively correlated with the perception that
nanotechnology’s benefits outweigh its risks, a finding consistent with public opinion
studies (Cobb and Macoubrie 2004, Macoubrie 2006). Increasing the knowledge base
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by providing more information may lead to polarization of views (cultural cognition1):
People tend to base their beliefs about benefits and risks of an activity on their cultural
appraisals of these activities (Wildavsky and Dake 1990, DiMaggio 1997). Applied to
nanotechnology, Kahan et al. (2009) found that predispositions towards nanotechnology
affect information selection and interpretation. Other factors influencing individual risk
perceptions are personal experience with the technology and judgement of one’s
reference group (Renn 1990). Analyses of socio-demographic variables show
differences in risk perception particularly with regard to gender (Pidgeon 2007)2. Thus,
the way people develop and express perceptions of risk is determined by individual,
social, cultural and situational factors. In conclusion, risk perception is a complex
construct and there is a whole range of variables that may explain some part of variance.
In our study we look at four technologies that differ regarding familiarity and the degree
of public discussions being controversial. We analyse individuals who differ regarding
their values and science orientation (self-selection, socialization). Finally, we take
gender as the important socio-demographic variable into account.
3 Self-Selection and Socialization Self-selection refers to individuals selecting themselves into a group. For self-selection
to happen there has to be a choice between alternative options such as between jobs or
between study areas. Socialization refers to the process by which values, attitudes and
practices of individuals are brought into line with those of the group they belong to.
Already when enrolling in university and selecting a subject, students of various
disciplines display significant differences regarding values: “Students choose a subject
the disciplinary culture of which has an affinity to their own values and norms or, 1 "Cultural cognition refers to the tendency of individuals to conform their beliefs about disputed matters of fact (e.g., whether global warming is a serious threat; whether the death penalty deters murder; whether gun control makes society more safe or less) to values that define their cultural identities." http://culturalcognition.net/, accessed 15.06.09 2 Other variables are e.g., income and race; Flynn et al (1994) call the combined effect of race and gender the ‘White male effect’.
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alternatively, reject subjects with an image that stands in contrast to their own
orientations” (Windolf 1995, 225). Unlike the USA, UK or France, Germany still has a
relatively homogeneous university sector (Windolf 1995, 208). Even if this is about to
change (Deutschland magazine 2008), so far a key determinant for enrolment in a
university is the subject studied. Choosing a subject to study (self-selection), be it
sciences, engineering, business, culture or social relations, is associated with cognitive
orientations, values and norms. Students enrolling in different subjects differ regarding
career expectations, cognitive abilities, preferred lifestyle and with respect to their
attitude towards science (Zarkisson and Ekehammar 1998). This attitude may vary over
time.
During their studies, students do not only acquire specialized knowledge but are also
exposed to the standards, supervision and peer culture of their disciplines amongst
which are considerable differences (Weidman et al. 2001). That disciplinary culture as a
‘code of ethics’ is important for the production, acquisition and use of knowledge
(Windolf 1995, 210).
Trautwein and Lüdtke (2007) analyzed the relationship between study field chosen and
students’ epistemological beliefs for beginners (self-selection) and for advanced
students (socialization). The results indicate that both self-selection and socialization are
at work in the context of attitudes towards science: Certainty scores, i.e. high scores
indicating the belief that scientific knowledge is certain and not subject to change, were
lower for ‘soft’ disciplines like humanities, arts, and social sciences and decreased with
time. Risks are matters of social conflict, and the definition of ‘the problem’ provides
legitimacy (Dietz et al. 1989) for positions (pro or against a technology) and actions
(promoting research or destroying genetically modified (GM) plots). The social identity
approach posits that people adopt attitudes and beliefs typical for their group as their
own (Wood 2000, 557). Socialization then contributes to the development of
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perceptions and goals which are of course key to actions and strategies of people in
various positions. Thus, self-selection and socialization are potentially important factors
in the explanation of attitudes and behaviours and the understanding of risk perception.
4 Technologies New technologies are associated with benefits and risks. Fierce debates about for
example nuclear energy or GM crops and food show that in many societies, ‘the public’
does not welcome technologies without reservation but demands debates on their
implications and potential hazards (Frewer 2003). Some technologies are more
controversial which means there is an ongoing discussion about its risks, while others
are less controversial where the perceived advantages clearly dominate perceived risks.
With technologies (as opposed to ‘nature’) creating environments and new risks, the
associated increased complexity and uncertainty makes technological developments less
and less predictable and manageable: According to Beck (1986) we live in a risk
society.
In what follows we sketch some of the opportunities and threats associated with those
technologies considered in our survey, namely renewable energies, nanotechnologies,
ICT, and genetic engineering. These technologies are part of the so-called high
technologies sector. They are key change drivers and possible convergence of high
technologies such as nanotechnology, modern biology, and ICT “will bring about
tremendous improvements in transformative tools, generate new products and services,
enable opportunities to meet and enhance human potential and social achievements, and
in time reshape societal relationships” (Roco 2007). As detailed below, the four
technologies discussed here differ regarding people’s knowledge, expectations, and
concerns associated with the technologies.
Modern Biotechnologies, Nanotechnologies and ICT are categorized as general purpose
technologies (Sheehan et al. 2006, Ruttun 2007) which include (i) pervasiveness, i.e.
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they may be used in a large number of industries, (ii) innovation spawning, i.e. the
technology leads to innovations in application sectors, (iii) complementarities in the
sense that innovation processes between upstream and downstream sectors are linked
(Bresnahan and Trajtenberg 1995, Helpman 1998). Referring to the reorganization of
work-life processes, Lipsey et al. (1998) highlight the societal implications of general
purpose technologies.3
The term renewable energies covers forms of energy generated from resources that are
naturally replenished such as sunlight, wind, water, or geothermal heat. Non-renewable
energies are naturally scarce and are associated with huge environmental burden. Lower
dependency on foreign energy sources, greening of industries and increasing public
environmental awareness are key drivers for the development and diffusion of
renewable energies (Greenwood et al. 2007). However, the materials, industrial
processes, and construction equipment used to create them may generate waste and
pollution. Thus, some renewable energy systems may create environmental problems.
Nevertheless, renewable energies are perceived as strongly contributing to resolving
environmental problems and securing energy supply. Risks are mostly discussed in the
context of investment failure (UNEP 2006) which could hamper further development of
the technology: “the risk profiles of renewable technologies differ significantly from
those of fossil fuel and nuclear plants. In particular, use of renewable energy options
generally pose little or no environmental, fuel price or security risks” (Rickersen et al.
2005, 47).
All in all, renewable energies have a positive image, there are hardly any risks perceived
but significant benefits.
3 Examples are e.g. the societal impact of electricity: For the first time this made people independent from daylight which to restructuring of production processes, allowed for shift work and hence also impacted on daily routines of entire families (Lipsey et al. 1998). Another example: The ongoing penetration of ICT allows for ‘mobile’ offices thereby also leading to a restructuring of business routines which in the end also spill over beyond professional activities.
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ICT covers technologies for the generation, transmission, storage and manipulation of
information and communication. During the last decades the wide-spread diffusion of
ICT and its rapid further development had a great impact on societies and ICT are still
major drivers of economic and social change, however, “Industry’s goal of digital
content “anywhere, anytime and on any device” is still remote“ (OECD 2008). The
implementation of ICT also plays a major role in the shift towards knowledge-based
societies, but “as the digital access divide decreases a digital use divide is emerging”
(OECD 2008).
So far the risks inherent in ICT as perceived by the public are not very extent. Most
objections refer to societal risks such as loss of control, technological dependence or
surveillance associated with ‘smart objects’. In sum, ICT have mainly a positive image,
there are some societal risks associated with them.
Genetic engineering “refers to the process of inserting new genetic information into
existing cells for the purpose of modifying one of the characteristics of an organism”
(United Nations 1997). It plays a key role in many areas such as agriculture, food,
medicine, and chemical industry. While many actors and institutions support its
developments, others oppose it fiercely. Worldwide, albeit to a different degree, it has
been debated very controversially. The issues cover economical, ethical, health and
social concerns.
The application of genetic engineering to the agro-food sector and the health sector is a
prominent example of the importance and complexity of stakeholder issues. While
medical applications are favorably, even uncritically, judged (TAB 2002), genetically
modified food is seen as not necessary or even as being dangerous. However, the
knowledge about genetic engineering can be described as vague, with little connection
between bits of knowledge (Eurobarometer, Pfister et al. 2000). Genetic engineering is
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controversially discussed; risks are perceived with regard to health, the environment and
society (e.g. human enhancement).
The term nanotechnologies covers technologies and devices working at an atomic and
molecular scale (dimensions smaller than 100 nanometers). The manipulation of
nanostructures allows for ongoing miniaturization, leads to using newly discovered
properties of materials and provides multiple possibilities in animate and inanimate
contexts. Nanotechnologies form part of technological platforms (Robinson et al. 2006).
While genetic engineering is based on the ‘code of life’, Nanotechnologies are
concerned with molecular structures. Thus, both technological fields really are at the
centre of ‘things’ and are general purpose technologies. They differ with regard to the
public awareness: Genetic engineering has been discussed for more than three decades,
whereas nanotechnologies are hardly known by the public (Kahan et al. 2009).
Nanotechnologies are discussed controversially; however, so far, most people are not
familiar with the technologies.
5. Empirical Study 5.1 Context of the Study and Propositions
In 2005 the European Commission published the results of an empirical study on
Europeans, Science and Technology. Citizens from 25 European countries were asked
about their knowledge (including a knowledge quiz), interests and perceptions regarding
science and technologies. Aiming at a representative study of citizens of 15 years of age
and over (Eurobarometer 224, 2005, 130) and assessing variables such as age, gender,
education and occupation, results for a number of socio-demographic groups are
available. The report concludes that “Europeans consider themselves poorly informed
on issues concerning science and technology” and that “the gap between science and
society still exists. Efforts must namely be made in order to bring science and
technology closer to certain categories of people who are less exposed to the scientific
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field, and who therefore have a more sceptic perception of science and technology”
(Eurobarometer 224, 2005, 125). However, detailed analyses of specific population
groups are not carried out.
Such an investigation of special groups has been performed by Lüthje (2008):
differentiating between people with a technical and an economic background, Lüthje
asked engineering and business administration students (beginners and advanced
students) and professionals (engineers and managers) about various aspects of
cooperation (amongst others: task preferences, information style, risk attitude in
innovation projects, goal orientation and time preferences). With regard to risk attitude
in innovation projects4 there are no significant differences between engineering and
business student beginners, but in the group of advanced students and in the group of
professionals, (prospective) engineers display a lower preference for (financial) risks
than (prospective) managers. However, risk has been limited to financial risk of
innovation projects. Trautwein and Lüdtke (2007) analyzed the relationship between
study field chosen and students’ epistemological beliefs and identified effects of both
self-selection and socialization.
In our study we analyze German students’ risk perception of four technologies. The
students differ regarding their major study area (‚tech’ and ‚non-tech’: self-selection)
and regarding the study progress ‚beginners’ and ‚advanced’: socialization). The four
technology fields under study are renewable energies, genetic engineering,
nanotechnology and ICT.
We propose that
Proposition 1: Self-Selection
4 Assessed through three items, e.g. “I prefer projects with relatively low risk (and moderate, but certain profit)“.
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Students who choose a technical field perceive lower health, environmental and
societal risks than students who choose a non-technical field.
Proposition 2: Socialization
(a) Advanced students in a technical field perceive lower health,
environmental and societal risks than beginners in a technical field
(b) Advanced students in a non-technical field perceive higher health,
environmental and societal risks than beginners in a non-technical field
Proposition 3: Inter-technology variation
The effects of self-selection and socialization will hold for all the four
technologies and for all types of risks investigated here.
5.2 Data
Sample
In our study we analyze German students’ risk perception of four technologies. The
students differ regarding their major study area in technical and non-technical fields
thereby also reflecting some kind of self-selection. Socialization comes into play since
we also distinguish between beginners and advanced students the latter referring to 3rd
term students and above. The four technologies under study are renewable energies,
genetic engineering, nanotechnology and ICT and we distinguish for each technology
the fields health, society and environment.
The total sample consists of 1400 questionnaires (owing to missing values, the number
of answers varies slightly with the questions). We collected the data within three
months (December 2007 to February 2008), from three North German universities
(Lüneburg, Hamburg and Flensburg). The non-technical study areas include Cultural
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Studies, Education (teaching), Social Sciences, Business Studies, and Economics. In the
following students in these field will be denoted as ‘non-tech’. The technical study
fields covered engineering: general, construction, water, ship building, and we denote
those students ‘tech’. Table 1 gives an overview over the sample.
Table 1 about here
Knowledge and Familiarity with the Technologies
Are people overconfident, i.e., do they think they are more knowledgeable than they
actually are?5 In the context of risk perception, overconfidence may lead to an overly
optimistic or pessimistic view on a technology. For example, being familiar with
renewable energies on account of reports in the media that it is a desirable approach to
energy generation may lead to people thinking that they know a fair amount about the
technologies involved and attributing low risk to the respective technologies. Similarly,
being aware of the controversial discussions around genetic engineering may lead to
attributing high risk to the technology.
We distinguished between two types of knowledge: Participants were asked to indicate
how well they are informed about the four technologies (‘familiarity’ – or self-assessed
knowledge, Table 2) and they completed a knowledge quiz (factual knowledge,
Table 3). Note that correlation analysis shows highly significant correlations between
knowledge (both self-assessed and factual) about science and technology and the choice
of any field of study: Those students choosing a technical field also dispose of more
knowledge on technological topics.
5 Alba and Hutchinson (2000, 123) analyze that proposition with respect to consumers: “Are consumers overconfident?”.
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Table 2 about here
Respondents seem to be more familiar with ICT and renewable energies and less
familiar with genetic engineering and particularly with nanotechnologies. It is
remarkable that the most common rating of familiarity with nanotechnologies is 1, that
is 296 respondents (21%) indicated that they are not at all informed. Nanotechnologies
and particularly genetic engineering are also seen more controversially.
Out of the four technologies, nanotechnology is the least understood technology, genetic
engineering is the most controversial technology, respondents indicate higher
familiarity with ICT and renewable energies and the latter is seen as least risky.
Table 3 about here
Four questions of the knowledge quiz (2, 3, 4, 5: Tab. 3) were adapted from the
Eurobarometer (2005), and four additional questions related to the four technologies
investigated here. The highest percentage of right answers is given for the question on
radioactivity (4), followed by the question on genetic engineering (1). Radioactivity
and genetic engineering are issues that have been discussed intensely in the media. The
lowest percentage of right answers is given for the question on nanotechnology (7). This
corresponds well with the self-assessed knowledge where nanotechnology also ranks
last. Compared with the Eurobarometer 2005 (see last column of Table 3) the
percentage of right answers is for all four questions higher in this survey.6 Both
6 However, in the three years between the Eurobarometer survey and our survey, discussion went on and the respondents in our survey may have taken notice of these discussions.
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familiarity ratings and knowledge scores vary with gender, study field and study
progress (Table 4).
Table 4 about here
The average knowledge scores (last column of Table 4) are higher for students in a
technical field than in a non-technical field. The same holds for the mean ratings of
familiarity with regard to renewable energies and nanotechnologies. A Mann-Whitney-
U-Test7 reveals that these differences between students in a technical and in a non-
technical field are significant. This is not true for genetic engineering. In conclusion, the
groups in different study areas investigated here show significant differences regarding
their knowledge scores and their familiarity with technologies except for genetic
engineering.
Risk perception of the technologies
For each technology, the respondents were asked to rate the health risk, environmental
risk and the societal risk as follows (example here: type of risk = health risks and
technology = genetic engineering):
I rate the health risks of genetic engineering as … 1-no risk at all to 11- very
high risk
Table 5 reports the mean ratings of health risks for the total sample and by gender,
study field and study progress.
Table 5 about here
7 The Mann-Whitney-U-Test is a non-parametric test for assessing whether two independent samples of observations come from the same distribution.
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For each technology, i.e., in each column (in bold), the highest average group rating is
in a female group and the lowest average group rating is in a male group. For every
group, i.e., in each row, renewable energies has the lowest rating and genetic
engineering has the highest. Thus, in the sample is consensus about which is the least
risky and which is the most risky technology.
Risk perception and self-selection
Proposition 1 states that students in a technical area perceive risks to be lower. Since the
majority of students in the technical area is male and gender is important in risk
perception, each gender group is analyzed separately.8 Figure 1 shows the mean ratings
for female students in the technical (N = 100) and non-technical (N = 645) study area.
Figure 1 about here
There are no significant differences in risk perceptions between the two female groups
with regard to highly controversial genetic engineering and the ‘no risk’ renewable
energies. However the differences in mean ratings are significant (p<0,05) for the risks
associated with nanotechnologies (all types: health, environment and society) and for
perceived societal risks of ICT: non-technical female students perceive those risks to be
higher than technical female students.
Figure 2 shows the mean ratings for male students in the technical (N = 356) and non-
technical (N = 207) study area.
8 Most of the variables are not normally distributed (Kolmogorov-Smirnov test), therefore the Mann-Whitney-U test was used to assess differences in ratings.
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Figure 2 about here
The differences between the two male groups are significant (p<0,05) for both the risks
associated with nanotechnologies and for genetic engineering (all types: health,
environment and society): non-technical male students perceive those risks to be higher
than technical male students.
Thus, students selecting themselves into a technical versus non-technical study area,
differ in their risk perception of two out of the four technologies analyzed here.
Proposition 1:
Students who choose a technical field perceive lower health, environmental and societal
risks than students who choose a non-technical field,
is supported for those technologies for which significant differences in risk perceptions
exist. Put differently, if risk perceptions differ significantly between tech and non-tech
groups, it is the non-tech group that perceives risks to be higher. This result holds for
both male and female students.
Risk perception and socialization
Proposition 2 refers here to the development of attitudes and perceptions during
students’ studies. Depending on their study area, risk perceptions are expected to
increase (non-technical area) or decrease (technical area), that is, pre-existing
perceptions will be amplified as a consequence of socialization. Hence we expect first-
term students in a technical field to display higher risk perceptions than advanced
students in a technical field: During their studies students become more familiar with
the technical side of technologies, they identify themselves with their study subject and
adopt attitudes and beliefs typical for their group as their own. With the same reasoning,
we expect first-term students in a non-technical field to display lower risk perceptions
than advanced students in a non-technical field. Students in the area of cultural and
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societal studies get more exposed to the non-technical side of technology including
topics such as stakeholders’ positions and society’s acceptance. Following the logic of
the cultural cognition hypothesis we thus expect that any initially existing risk
perception to be amplified as a consequence of socialization. We therefore compare for
technical students the mean rating of the two groups ‘beginners’ and ‘advanced’ (see
Figure 3) and do the same for students in non-technical fields of study (see Figure 4).
Figure 3 about here
Fig. 3 shows that in a technical area beginners rate risks higher than advanced students;
the differences between mean ranks (Mann-Whitney-U test) are significant for
nanotechnologies (health, environment, society), ICT (health, environment, society) and
genetic engineering (society).
Proposition 2 (a)
Advanced students in a technical field perceive lower health, environmental and
societal risks than beginners in a technical field,
is supported for those technologies for which significant differences in risk perceptions
exist: if risk perceptions differ significantly between beginners and advanced students, it
is the beginner group that perceives risks to be higher.
Figure 4 about here
In the non-technical area, beginners rate risks higher than advanced students; the
differences between mean ranks (Mann-Whitney-U test) are significant for all risk
perception variables except for renewable energies (health, environment, society) and
nanotechnologies (health).
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Proposition 2 (b)
Advanced students in a non-technical field perceive higher health, environmental and
societal risks than beginners in a non-technical field,
is not supported; if risk perceptions differ significantly between beginners and advanced
students, it is again the beginner group that perceives risks to be higher.
In conclusion, study progress is associated with lower risk perceptions, regardless of the
study area (technical or non-technical).
The results so far also show that Proposition 3
The effects of self-selection and socialization will hold for all the four technologies and
for all types of risks investigated here,
is not supported: The effects of self-selection and socialization do not hold for all the
four technologies.
5.3 Relationship between risk perception, study area and study progress
Dimensions of risk perception
As discussed above, the mean ratings are highest for genetic engineering and lowest for
renewable energies. This holds for all the risks studied (environmental, health, societal
risks). Indeed, a factor analysis of risk perception variables shows that it is the
technological areas and not the types of risk that are the relevant dimensions of risk
perception (Table 6).
Table 6 about here
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The factor loadings are based on the twelve questions on risk perception of four
technologies regarding the three areas environment, health and society. The grouping of
the high factor loadings leads to the four factors (i) ‘Risks associated with renewable
energies’: RiskRenE, (ii) ‘Risks associated with nanotechnologies’: RiskNano, (iii)
‘Risks associated with ICT’: RiskICT and (iv) ‘Risks associated with genetic
engineering’: RiskGenE.
Regression analyses
Specifying each factor from the factor analysis as a dependent variable, and study area
and study progress as independent variables, regression analyses show the relationship
between these variables (Table 7):
Risk perception (technology) = constant + b1 · study area + b2 · study progress.
Analyses were performed for male and female respondents separately. Significant
results are in bold numbers.
Table 7 about here
The regression analyses show no significant results for risk perceptions concerning
renewable energies. Concerning risk perception of nanotechnologies, the selection
variable (study area) is significant for both male and female students, indicating that
students in a technical area perceive lower risks than students in a non-technical area.
Study progress is only significant for female students with advanced students perceiving
lower risks. With respect to risk perception of genetic engineering, the selection variable
is significant only for male students, that is, male students in a technical field perceive
lower risks than male students in a non-technical field. In contrast to that, study
progress is significant only for female students: female advanced students perceive
21
lower risks than female beginners. With regard to ICT, both female and male advanced
students perceive lower risks than beginners.
6 Discussion As illustrated above, the relationship between self-selection and socialization on the one
hand and risk perception of technologies on the other varies between technologies. The
results presented show that there is consensus amongst the groups about renewable
energies posing hardly any risk and genetic engineering being the most risky technology
of the four technologies investigated here. However, there are differences regarding the
level of risk perception.
Renewable energies have a positive image, people indicate a relatively high degree of
familiarity, there are hardly any risks perceived; this holds for all groups analyzed here.
There are no significant differences in risk perception between different study areas or
with regard to study progress.
As shown in Table 2, nanotechnology is the least understood technology with a median
familiarity ranking of 3. However, it is also the technology for which the range of
average familiarity rating between female first year students in a non-technical area
(2,64) and male advanced students in a technical area (5,54) is greatest (see Table 4). In
this case, higher familiarity goes with lower risk perception. Genetic engineering is the
most controversial technology and for all but one group, familiarity is rated as high as
or higher than nanotechnologies. Since genetic engineering is not part of the technical
study areas investigated here, it is neither a particular interest in that technology, nor a
growing familiarity owing to studying the topic that could account for differences in
familiarity. Rather, it might be the exposure to discussions in the media that lead to
respondents indicating similar levels of familiarity. Gender plays an important role: For
male students, risk perception differs between technical and non-technical areas;
however, this is not the case for female students. With regard to ICT, in both the
22
technical and the non-technical area advanced students perceive lower risk than
beginners. ICT is a general purpose technology that is wide-spread and many people are
accustomed to using it on a daily basis. Performing studies at university usually comes
with intense usage of ICT which might put risks into a different perspective.
The results of this study suggest that selection and socialization effects on risk
perception vary between technologies: For non-controversial technologies, risk
perception is homogeneous. For controversial technologies with affinity to an area of
study, students in that area perceive lower risks than students in other areas, even as
beginners. For controversial technologies with no affinity to an area of study, gender
plays an important role.
7 Conclusion Pupils are taught every day systematically, i.e., according to a specified, often nation-
wide schedule. In its report ‚Rising above the gathering storm: Energizing and
employing America for a brighter economic future‘(National Academies of the USA
2005), the first recommendation is to “increase America’s talent pool by vastly
improving K-12 science and mathematics education” (p. 5). However, the relationship
between knowledge and technological development is not straightforward: Participating
in the creation of technological paths, people’s intentions, strategies and actions are
partly influenced by how chances and risks of the technology are perceived: Risk
perception plays a crucial role in technology development. It is not only “science and
mathematics” but equally an understanding of risks and chances and the way
perceptions develop that could “bring science and technology closer to certain
categories of people who are less exposed to the scientific field, and who therefore have
a more skeptic perception of science and technology” (Eurobarometer 224, 2005, 125).
Students select themselves into an ongoing learning process and choose a field of study.
This self-selection partly will reflect attitudes towards science, preferences for topics
23
and career expectations. Going to university, the teaching and learning of subjects
become less uniform. Each discipline has its own culture and its ways for producing and
using knowledge. Socialization processes may contribute to the development of
‘typical’ perceptions.
In an organization people take on roles and tasks: a financial controller, a researcher and
a marketing manager differ in their screening and evaluation of innovations and in their
level and type of information. In general, scientists and developers may be better
informed about technical aspects, marketing managers may be better informed about
user needs and usage patterns. Homophily (Lazarsfeld and Merton 1954) between
members of a group (or a department in an organization) may strengthen attitudes and
confirm perceptions. This may affect intra-organizational interaction between managers
of different departments as well as inter-organizational interaction. For example, Kim
and Higgins (2007, 510) propose that “the prominence of members’ prior careers
influenced the rate at which companies form alliances”.
It is this kind of knowledge about selection and socialization processes that could
further the understanding of cooperation partners’ perceptions as well as differences in
perceptions.
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29
Table 1: Overview of Sample, N=1400
Variable Valid %
Sex
Female
Male
57.3
42.7
Age
≤ 20
21-25
26-30
> 30
33.5
52.6
8.2
4.7
Field of studies
‚Non-Tech‘
‚Tech‘
65.8
34.2
Study progress
Beginners
Advanced
79.2
20.8
30
Table 2: Familiarity with Technologies: Ratings
I am informed about … renewable energies … as follows
renewable energies
ICT genetic engineering
nano technologies
Mean 6.39 6.05 5.27 3.69
Median 7 6 5 3
Modus 8 6 4 1
SD 2.26 2.46 2.19 2.33
N 1399 1399 1398 1398
Scale: 1 = not at all informed, 11 = very well informed
31
Table 3: Quiz results: factual knowledge
%
Don’t know
%
wrong
answer
%
right
answer
%
right answer
EU 2005
1 Naturally, tomatoes have genes 9.2 10.8 80.0 n.a.
2 Lasers work by focusing sound waves. 28.4 13.9 57.7 47
3 Antibiotics kill viruses as well as
bacteria
11.4 21.3 67.2 46
4 Radioactive milk can be made safe by
boiling it
14.1 0.7 85.3 75
5 Electrons are smaller than atoms. 10.4 21.3 68.2 46
6 For a certain irradiation angle of the
sun, the power generation of a
photovoltaic power plant will be higher
in the summer than in the winter
35.6 27.9 36.5 n.a
7 With the scanning tunneling
microscope it is possible to move single
atoms
63.7 26.0 10.3 n.a.
8 With respect to speed fiberglass
technology is superior to copper
35.4 6.0 58.5 n.a
32
Table 4: Familiarity and knowledge by study area, study progress and gender: Mean
ratings (1-11) and scores (0-8)
Familiarity (1-11) Quiz
Group (N) RenEn GenEng Nano ICT Score
(0-8)
NonTechBeginnersF (520) 5.69 5.42 2.64 5.35 3.75
NonTechAdvancedF (76) 5.48 4.51 3.08 6.13 3.68
NonTechBeginnersM (143) 6.80 5.48 3.93 6.74 4.70
NonTechAdvancedM (35) 6.46 5.20 4.03 6.89 4.89
TechBeginnersF (74) 7.20 6.01 4.24 5.66 5.31
TechAdvancedF (26) 7.23 5.00 4.85 5.58 5.92
TechBeginnersM (240) 7.30 5.13 5.13 6.82 5.94
TechAdvancedM (114) 7.86 5.17 5.54 6.91 6.04
Total (1228) 6.46 5.31 3.75 6.06 4.67
RenEn=renewable energies, GenEng=genetic engineering, Nano=nanotechnologies,
ICT=Information & Communication Technologies
Study area: non-technical field (NonTech), technical field (Tech)
Study progress: first-term (Beginners), third-term and above (Advanced)
Gender: female (F), male (M)
33
Table 5: Mean ratings of health risks by study area, study progress and gender
I rate the … health risks … as follows
renewable
energies
genetic
engineering
nano-
technologies ICT
Mean 3.02 7.43 5.93 4.76Non-tech BeginnersFf.
N 508 512 477 503
Mean 2.87 6.88 5.53 4.11Non-tech AdvancedF.
N 76 76 74 76
Mean 2.71 7.21 5.27 4.82Non-tech BeginnersM.
N 142 142 139 141
Mean 2.51 6.97 5.26 3.89Non-tech AdvancedM.
N 35 35 35 35
Mean 2.87 7.57 5.49 5.11Tech BeginnersF.
N 74 74 74 74
Mean 2.46 6.31 4.19 3.73Tech AdvancedF.
N 26 26 26 26
Mean 2.50 6.29 4.46 4.35Tech BeginnersM.
N 240 239 235 238
Mean 2.43 6.40 3.90 3.61Tech AdvancedM.
N 113 113 114 113
total Mean 2.78 7,02 5,25 4,51
N N 1214 1217 1174 1206
1-no risk at all … 11 very high risk
Study area: non-technical field (NonTech), technical field (Tech)
Study progress: first-term (Beginners), third-term and above (Advanced)
Gender: female (F), male (M)
34
Table 6: Risk Perception for four technologies in three areas: factor loadings
Factor
I RiskRenE II RiskNano III RiskGen IV RiskICT
Health Risks
RenEn 0.848 0.113 -0.006 0.058
GenEng 0.027 0.195 0.860 0.060
Nano 0.095 0.816 0.242 0.082
ICT 0.091 0.036 0.152 0.816
Environm. Risks
RenEn 0.861 0.032 0.056 0.036
GenEng. 0.000 0.154 0.814 0.166
Nano 0.088 0.792 0.252 0.178
ICT 0.025 0.208 0.127 0.753
Societal Risks
RenEn 0.834 0.097 -0.003 0.035
GenEn 0.020 0.280 0.663 0.182
Nano 0.101 0.809 0.158 0.222
ICT 0.015 0.167 0.085 0.696
RenEn=renewable energies, GenEng=genetic engineering, Nano=nanotechnologies, ICT=Information &
Communication Technologies
I – Risk renewable energies (RiskRenE), II- Risk nanotechnologies (RiskNano), III – Risk genetic
engineering (RiskGenE), IV – Risk ICT (RiskICT)
35
Table 7: Regression Analyses:
Risk Perception = const. + b1 (selection) + b2 (study progress)
Technology (factor values)
Sample, N
constant (significance
level)
b1 selection 1 = tech
b2 study progress
1 = beginners
R, R2
All
1165
-0.011
(0.874)
-0.072
(0.241)
0.062
(0.398)
0.047, 0.002
(0.278)
Male
513
-0.090
(0.404)
-0.014
(0.883)
0.085
(0.381)
0.04, 0.002
(0.658)
RiskRenE
Female
626
0.027
(0.802)
-0.083
(0.460)
0.027
(0.807)
0.033, 0.001
(0.717)
All
1165
0.029
(0.675)
-0.614
(0.000)
0.234
(0.001)
0.329, 0.108
(0.000)
Male
513
-0.181
(0.091)
-0.449
(0.000)
0.180
(0.062)
0.235, 0.055
(0.000)
RiskNano
Female
626
0.086
(0.363)
-0.412
(0.000)
0.274
(0.006)
0.207, 0.043
(0.000)
All
1165
-0.23
(0.749)
-0.208
(0.001)
0.141
(0.050)
0.126, 0.016
(0.000)
Male
513
0.081
(0.463)
-0.288
(0.003)
-0.016
(0.875)
0.133, 0.018
(0.011)
RiskGenEn
Female
626
-0.153
(0.128)
0.114
(0.286)
0.283
(0.008)
0.110, 0.012
(0.022)
All
1165
-0.283
(0.000)
-0.071
(0.235)
0.419
(0.000)
0.184, 0.034
(0.000)
Male
513
-0.306
(0.004)
-0.111
(0.221)
0.451
(0.000)
0.221, 0.049
(0.000)
RiskICT
Female
626
-0.277
(0.008)
0.068
(0.535)
0.401
(0.000)
0.146, 0.021
(0.001)
36
Figure 1: Risk perception of female students in technical and non-technical study areas
1
2
3
4
5
6
7
8
GE-H GE-E GE-S NT-H NT-E NT-S ICT-H ICT-E ICT-S RE-H RE-E RE-S
risk
leve
lNon-Tech Tech
GE=genetic engineering, NT=nanotechnologies, ICT=information and communication
technologies, RE=renewable energies
H=health, E=environment, S=society
Figure 2: Risk perception of male students in technical and non-technical study areas
GE=genetic engineering, NT=nanotechnologies, ICT=information and communication technologies, RE=renewable energies
H=health, E=environment, S=society
2
3
4
5
6
7
8
GE-H GE-E GE-S NT-H NT-E NT-S ICT-H ICT-E ICT-S RE-H RE-E RE-S
risk
leve
l
Non-Tech Tech
37
Figure 3: Risk perception of students in technical study areas: beginners and advanced
students
2
3
4
5
6
7
8
GE-H GE-E GE-S NT-H NT-E NT-S ICT-H ICT-E ICT-S RE-H RE-E RE-S
risk
leve
l
TechBeg TechAdv
GE=genetic engineering, NT=nanotechnologies, ICT=information and communication technologies, RE=renewable energies
H=health, E=environment, S=society
Figure 4: Risk perception of students in non-technical study areas: beginners and
advanced students
012
3456
78
GE-H GE-E GE-S NT-H NT-E NT-S ICT-H ICT-E ICT-S RE-H RE-E RE-S
risk
leve
l
NonTBeg NonTAdv
GE=genetic engineering, NT=nanotechnologies, ICT=information and communication
technologies, RE=renewable energies
H=health, E=environment, S=society
